Dynamics in the international market segmentation of new product growth

Lemmens, Aurélie; Croux, Christophe; Stremersch, Stefan

doi:10.1016/j.ijresmar.2011.06.003

Cited by 32 publications

(6 citation statements)

References 67 publications

(78 reference statements)

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“…The modern approaches to the analysis of economic dynamics (Lemmens, Croux and Stremersch, 2012) require applying efficient tools, which would not be limited to detecting common regularities and correlations and would provide the researcher with the opportunity to detect also the "latent trends". This would allow to solve problems related to usage of the analysis results at planning and forecasting more efficiently, thus minimizing alternative losses of market players.…”

Section: Discussionmentioning

confidence: 99%

About the Method of Analysis of Economic Correlations by Differentiation of Spline Models

Ilyasov¹

2014

MAS

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The article considers spline approximation as one of efficient methods of modeling economic dynamics. Spline approximation of economic dynamics allows carrying out qualitative and accurate transition from discrete values of a lattice function to a continuous model of a process, which allows calculating values of a studied index at any time point (interpolation). Spline representation improves the quality of economic dynamics modeling while saving the real values of the studied process at each time point. In this article, differentiation of spline models is used for analysis of the economic indexes growth rate. Correlations are detected and itemized by comparison of derivatives. The possibility of detecting "latent trends" is demonstrated by differentiation of spline models of the dynamics using the example of economic indexes of the oil and gas market of Russia. For example, in the first case, we consider spline models of the dynamics of export prices for oil and natural gas. Here, the correlation of the studied indexes is obvious and is detected by both calculation of the correlation ratio and visualization of the studied rows of dynamics with spline models. As an opposite example, we consider the dynamics of the volumes of oil and natural gas export. In this case, we gain the correlation ratio close to zero, which is to evidence absence of correlation. Modeling of the studied dynamics with cubic splines also does not detect any correlation between the dynamics of volumes of the oil and gas export. Our assumptions about "latent trends" are also confirmed by differentiation of spline models – the correlation between the change rate of the volumes of the oil and gas export is detected. Use of spline functions at economic dynamics modeling is determined with such positive properties of theirs as continuity, flexibility, differentiability, the property of minimal curve, etc.

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Section: Discussionmentioning

confidence: 99%

About the Method of Analysis of Economic Correlations by Differentiation of Spline Models

Ilyasov¹

2014

MAS

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“…2) General values are terminal or ultimate values sought after throughout a customer's lifetime. [26] Lemmens (2012) [27] segmentation does not explain specific information on products but does explain long-term customer behavior. By contrast, the second type of market segmentation is specific segmentation, which is directly related to products or services, such as instrumental value, benefit, and usage pattern, which are the main variables that can be used as segmentation bases [13], [20].…”

Section: Literature Review On Market Segmentationmentioning

confidence: 99%

“…However, if these approaches use only one or two factors as segmentation bases, it is hard to provide sufficient information on a competitive market environment. Regarding these weak points, researchers have attempted more sophisticated approaches that can give more detailed and abundant information on markets and customers by combining certain bases (for example, values, benefits, lifestyles, and attitudes) over time [13], [23]- [27], [29]. However, this approach has some limitations because it is not actually a systematic method in that it only technically combines several segmentation bases with less consideration of each causal relation in view of customer behavioral studies.…”

Section: Literature Review On Market Segmentationmentioning

confidence: 99%

An Empirical Analysis on a Predictive Method of Systematic Segmentation in Volatile High-Tech Markets

Shin¹,

Jeon²,

Choi³

et al. 2013

ETRI J

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High-tech markets are unpredictable owing to rapid technology innovation, diverse customer needs, high competition, and other elements. Many scholars have attempted to explain the uncertainty in high-tech markets using their own various approaches. However, sufficiently clear ways to predict diverse changes and trends in hightech markets have yet to be presented. Thus, this paper proposes a new approach model, that is, systematic market segmentation, to give more accurate information. Using an empirical dataset from the mobile handset market in the Republic of Korea, we conduct our research model consisting of three steps. First, we categorize nine basic segments. Second, we test the stability of these segments. Finally, we profile the characteristics of the customers and products. We conclude that the approach is able to offer more diagnostic information to both practitioners and scholars. It is expected to provide rich information for an appropriate marketing mix in practice.

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“…In the context of the task of predicting the outflow of customers, the discovery of patterns that describe stable trajectories of consumer movement between clusters is one of the most important areas of research. For this, various dynamic models based on pattern identification with such methods as clustering [10,11], association rules [12], and hidden Markov models [13] are used.…”

Section: Introductionmentioning

confidence: 99%

Predicting customer churn based on changes in their behavior patterns

Zelenkov¹,

Suchkova²

2023

Bus. Inform.

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Customer retention is one of the most important tasks of a business, and it is extremely important to allocate retention resources according to the potential profitability of the customer. Most often the problem of predicting customer churn is solved based on the RFM (Recency, Frequency, Monetary) model. This paper proposes a way to extend the RFM model with estimates of the probability of changes in customer behavior. Based on an analysis of data relating to 33 918 clients of a large Russian retailer for 2019–2020, it is shown that there are recurring patterns of change in their behavior over a single year. Information about these patterns is used to calculate the necessary probability estimates. Incorporating these data into a predictive model based on logistic regression increases prediction accuracy by more than 10% on the metrics AUC and geometric mean. It is also shown that this approach has limitations related to the disruption of behavioral patterns by external shocks, such as the lockdown due to the COVID-19 pandemic in April 2020. The paper also proposes a way to identify these shocks, making it possible to forecast degradation in the predictive ability of the model.

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Dynamics in the international market segmentation of new product growth

Cited by 32 publications

References 67 publications

About the Method of Analysis of Economic Correlations by Differentiation of Spline Models

About the Method of Analysis of Economic Correlations by Differentiation of Spline Models

An Empirical Analysis on a Predictive Method of Systematic Segmentation in Volatile High-Tech Markets

Predicting customer churn based on changes in their behavior patterns

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